{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis\n",
    "This notebook provides basic functionality to analysis the given data. Data can be realized using multiple ways such as - data description in a data frame to understand the data organization, plots to understand statistical properties, principal component analysis to understand dominant features in the data. Here, we provide these functionality with minimal input from user. \n",
    "\n",
    "\n",
    "### Table of Content\n",
    "\n",
    "  * [Data Description](#Data-Description)\n",
    "  * [Import Data](#Import-Data)\n",
    "  * [Data Frame](#Data-Frame)\n",
    "  * [Statistics](#Statistics)\n",
    "  * [Principal Component Analysis](#Principal-Component-Analysis)\n",
    "  * [Feature Engineering](#Feature-Engineering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description\n",
    "\n",
    "NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data set (Turbofan Engine Degradation Simulation Data Set) is a widely used benchmark data. C-MAPSS data includes sensor data with different number of operating conditions and fault conditions [1].\n",
    "\n",
    "|      Data Set        |  FD001 | FD002 | FD003 | FD004|\n",
    "|----------------------|--------|-------|-------|------|\n",
    "|  Train Trajectories  |  100   |  260  |  100  | 249  |\n",
    "|   Test Trajectories  |  100   |  259  |  100  |  248 |\n",
    "| Operating Conditions |    1   |   6   |   1   |   6  |\n",
    "|    Fault Conditions  |    1   |   1   |   2   |   2  |\n",
    "\n",
    "The data has 4 sub-data sets with different number of operating conditions and fault conditions and each sub-data set is further divided into training and test subsets, as shown in table above. We use dataset FD002 for our purpose. Each row in the data is a snapshot of data taken during a single operating time cycle, which includes 26 columns: \n",
    " * 1st column represents engine ID, \n",
    " * 2nd column represents the current operational cycle number, \n",
    " * 3-5 columns are the three operational settings that have a substantial effect on engine performance, \n",
    " * 6-26 columns represent the 21 sensor values. More information about the 21 sensors can be found in [2]. \n",
    "\n",
    "The engine is operating normally at the start of each time series, and develops a fault at some point in time which is unknown. In the training set, the fault grows in magnitude until a system failure. In the test set, data is provided up to some time prior to system failure. The goal is to estimate the number of remaining operational cycles before failure on the test data. \n",
    "\n",
    "[1] S. Zheng, K. Ristovski, A. Farahat and C. Gupta, \"Long Short-Term Memory Network for Remaining Useful Life estimation,\" 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, 2017, pp. 88-95.\n",
    "\n",
    "[2] E. Ramasso and A. Saxena, “Performance benchmarking and analysis of prognostic methods for cmapss datasets.” International Journal of Prognostics and Health Management, vol. 5, no. 2, pp. 1–15, 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data\n",
    "\n",
    "'Turbofan Engine Degradation Simulation Data Set' can be downloaded from here: \n",
    "https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Training Data Loading...\n",
      "[Info] Testing Data Loading...\n",
      "[Info] Loading records of RUL on this testing data...\n"
     ]
    }
   ],
   "source": [
    "# Import C-MAPSS Dataset FD002 train and test and records of RUL\n",
    "# provide file path to this function  ---\n",
    "from SAP.data_import import get_C_MAPSS_Data\n",
    "data_training_FD2, data_testing_FD2, data_testing_RUL_FD2 = get_C_MAPSS_Data(path = 'Data', dataset = 'FD002')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns in dataframe: 26\n",
      "\n",
      "Columns are:-  engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 \n",
      "\n",
      "Top 5 lines in dataframe:\n",
      "   engine_id  cycle  setting1  setting2  setting3      s1      s2       s3  \\\n",
      "0          1      1   34.9983    0.8400     100.0  449.44  555.32  1358.61   \n",
      "1          1      2   41.9982    0.8408     100.0  445.00  549.90  1353.22   \n",
      "2          1      3   24.9988    0.6218      60.0  462.54  537.31  1256.76   \n",
      "3          1      4   42.0077    0.8416     100.0  445.00  549.51  1354.03   \n",
      "4          1      5   25.0005    0.6203      60.0  462.54  537.07  1257.71   \n",
      "\n",
      "        s4    s5   ...       s12      s13      s14      s15   s16  s17   s18  \\\n",
      "0  1137.23  5.48   ...    183.06  2387.72  8048.56   9.3461  0.02  334  2223   \n",
      "1  1125.78  3.91   ...    130.42  2387.66  8072.30   9.3774  0.02  330  2212   \n",
      "2  1047.45  7.05   ...    164.22  2028.03  7864.87  10.8941  0.02  309  1915   \n",
      "3  1126.38  3.91   ...    130.72  2387.61  8068.66   9.3528  0.02  329  2212   \n",
      "4  1047.93  7.05   ...    164.31  2028.00  7861.23  10.8963  0.02  309  1915   \n",
      "\n",
      "      s19    s20     s21  \n",
      "0  100.00  14.73  8.8071  \n",
      "1  100.00  10.41  6.2665  \n",
      "2   84.93  14.08  8.6723  \n",
      "3  100.00  10.59  6.4701  \n",
      "4   84.93  14.13  8.5286  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "from SAP.utils import dataframe\n",
    "# training dataframe\n",
    "dataframe(df = data_training_FD2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram Plots of every column variable:\n"
     ]
    },
    {
     "data": {
      "image/png": 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VAVBXV8c999yTO2UmcEdEbImIVcBKYKrj0MysY24cmZkVyC233MJpp52WX3S4pKWS/lvS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\nJAkwOZIkSZIkAP5//NG9mSkNecgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1236e3910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SAP.utils import stats_histogram\n",
    "%matplotlib inline\n",
    "# training data statistics\n",
    "stats_histogram(df = data_training_FD2,\n",
    "      c = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c829ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SAP.utils import stats_boxplot\n",
    "%matplotlib inline\n",
    "# training data statistics\n",
    "stats_boxplot(df = data_training_FD2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that sensors si (i = 1 to 21) values are distributed across a wide range. For modeling, it would be advised to scale the range and level to a common reference using normalization or standardization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Info] Training Data Loading...\n",
      "[Info] Testing Data Loading...\n",
      "[Info] Loading records of RUL on this testing data...\n",
      "PC coverage: [ 0.52,  0.48,  0.00,  ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1275591d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SAP.data_import import get_C_MAPSS_Data\n",
    "data_training_FD2, data_testing_FD2, data_testing_RUL_FD2 = get_C_MAPSS_Data(path = 'Data', dataset = 'FD002')\n",
    "\n",
    "from SAP.utils import pca\n",
    "%matplotlib inline\n",
    "pca(df = data_training_FD2,\n",
    "    cols = ['setting1','setting2','setting3']\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take three settings as operational conditions and observe that these settings are clustered in six groups in their principal component space. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
